1. Field of the Invention
The present invention relates to an image retrieval device and an image retrieval method to retrieve an image being similar to an inquired image, from images accumulated in an image database and a storage medium storing an image retrieval program to retrieve, by controlling a computer, an image being similar to an inquired image from images stored in an image database
The present application claims priority of Japanese Patent Application No. Hei 11-351657 filed on Dec. 10, 1999, which is hereby incorporated by reference.
2. Description of the Related Art
There are cases where an image being similar to a specified image (hereinafter referred to as an “inquired image”) is retrieved from an image database in which image data is accumulated and managed. Many conventional similar-image retrieving technologies of this kind have been proposed. In such conventional technologies, color information is mainly used as an image feature descriptor. Moreover, in most of the conventional technologies, a histogram of color information and a comparison process based on similarity obtained by the histogram is employed to retrieve a targeted image. However, this method has a problem in that a color structure of an image is not reflected.
To solve this problem, similar-image retrieval technologies are disclosed in, for example, Japanese Patent Application Laid-open Hei 8-249349 in which the image is retrieved with high accuracy by introducing an attempt to have the color structure of the image reflected in the image retrieval and where, in an image database, the image is divided into a plurality of blocks and then a typical color of each block is calculated as an image feature descriptor and a pattern matching is performed. However, in this conventional technology, since the typical color of each block is calculated, a scale of the image feature descriptor is made larger, causing a reduction in a retrieval speed. This also causes a size of a hardware required for retrieval processing to be made larger.
Another conventional method to solve this problem is to perform an orthogonal transform on the image to efficiently express the image feature. FIG. 4 is a schematic block diagram showing configurations of main parts of a conventional image retrieval device using a transform coefficient. As shown in FIG. 4, an image feature descriptor storing section 41 stores a transform coefficient 401 for various image data. A similarity calculating section 42 compares a transform coefficient 403 contained in inquired image data with an image feature descriptor 402 accumulated in the image feature descriptor storing section 41 to calculate similarity between them and outputs its result 404.
By performing an orthogonal transform of an image and by using a part of the coefficient as an image feature descriptor, a scale of the image feature descriptor can be made smaller. This allows the retrieval processing to be made high-speed and a size of a hardware to be made smaller.
Furthermore, another method to improve image retrieval accuracy is to decode a transform coefficient. FIG. 5 is a schematic block diagram showing configurations of another conventional image retrieval device which performs decoding of the transform coefficient. As shown in FIG. 5, the conventional image retrieval device is composed of an image feature descriptor storing section 51, an inverse orthogonal transforming device 52, a color space transforming device 53 and a similarity calculating section 54. The image feature descriptor storing section 51 stores, in advance, a transform coefficient 501 of image data as an image feature descriptor. The inverse orthogonal transforming device 52 performs an inverse orthogonal transform on an image descriptor 502 accumulated in the image feature descriptor storing section 51 and outputs transformed image data 503. The color space transforming device 53 transforms color space of image data 504 output from the inverse orthogonal transforming device 52. Inquired image data 505 is input as image data 506 whose color space is transformed by a color space transforming device 55 to the similarity calculating section 54. The similarity calculating section 54 calculates similarity between the input image data 506 and the image data 504 obtained from the color space transforming device 53 and outputs its result 507.
However, each of above conventional similarity image retrieving technologies has following problems:
In the conventional technology to try to express efficiently an image feature by performing an orthogonal transform on an image, though retrieval processing can be made high-speed and a size of a hardware required for image retrieval can be made smaller, the conventional technology cannot detect an image having a complete visual similarity or detects an image which is not visually similar in some cases.
This is because there is no conformity in a distance between images expressed by transform coefficients and visual similarity among images, causing insufficient retrieval accuracy
In the conventional technology to match an image obtained by reconstructing the image by decoding orthogonally-transformed coefficient and then mapping the image over color space such as a HSV (Hue, Saturation, Value) or a like, with an inquired image, though excellent retrieval accuracy can be obtained, the image retrieval is very costly and speed of the image retrieval is reduced. This is because it is necessary to perform decoding processing and color space transforming processing, at every time of the retrieval, on each of image feature descriptors of accumulated data.